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[matplotlib] Merge two graphs into one

twinx() import pandas as pd import matplotlib.pyplot as plt import numpy as np f, ax = plt.subplots(1,1) x_values = [0,1,2,3,4,5] y_values = [0,1,4,9,16,25] ax.plot(x_values, y_values, color='red', linestyle='dashed', linewidth=1, marker="o") x2_values = [0,1,2,3,4,5] y2_values = [1000,2000,3000,4000,5000,6000] ax2=ax.twinx() ax2.plot(x2_values, y2_values, color='darkblue', linestyle='dashed', l..

[pandas] merge/concat data-frame

Sometimes, two data set should be merged to provide more accurate information. Merging data frame is divided into two parts: vertical and horizontal merging. 1) Vertical merging import pandas as pd df1 = pd.DataFrame({'A' : [1, 2, 3], 'B' : [11, 12, 13], 'C' : [21, 22, 23]}) df2 = pd.DataFrame({'A' : [4, 5, 6], 'B' : [14, 15, 16], 'C' : [24, 25, 26]}) print(df.concat([df1, df2])) print(df.concat..

[pandas] Data transformation - using current data to classify

import pandas as pd df = pd.DataFrame({'a':[1,2,3,4,5]}) # create a 'b' column if a= 2)] df['b'][a.index] = 's4' a = df[df['a'] > 4] df['b'][a.index] = 'b4' print(df) # Second method. using 'Apply with function' def apply_function(a): if a < 2: return 's2' elif a < 4: return 's4' else: return 'b4' df['b'] = df['a'].apply(apply_function) # Map - when there is a certain categories without conditio..

[pandas] add new column / delete column

1. Column import pandas as pd df = pd.DataFrame({'a': [1,1,3,4,5], 'b': [2,3,2,3,4], 'c': [3,4,7,6,4]}) # 1. to simply add new column df['d'] = [1,3,6,4,8] # 2. add one number in that column (will be filled out with that number) df['e'] = 1 # +) calculated result can be also created as a new column. # check datatype first print(df.dtypes) df['f'] = df['a'] + df['b'] - df['c'] # delete a column d..

[pandas] Data type conversion (astype etc...)

import pandas as pd df = pd.DataFrame({'date' : ['5/11/21', '5/12/21', '5/13/21', '5/14/21', '5/15/21'], 'sales' : ['10', '15', '20', '25', '30'], 'visitors' : ['10', '-', '17', '23', '25'], 'temp.' : ['24.1', '24.3', '24.8', '25', '25.4']}) # we need to check data type for each column (to change/edit data) print(df.dtypes) # try to change the value without data conversion df['edited sales'] = d..

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